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Development and Validation of a Mood Measure for Adolescents

Peter C. Terry, Andrew M. Lane, Helen J. Lane, and Lee Keohane

Accepted for publication in Journal of Sports Sciences

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Abstract

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Introduction

The quest to understand the psychology of emotion has generated persistent investigation of the construct of mood. Substantial attention has been paid to self-reported mood states and their attendant impact upon behaviour. Such research relies heavily upon parsimonious methods of assessing transient emotions. To date, psychometric development in this area has focused primarily upon adult populations, particularly students and psychiatric out-patients. The purpose of the present study was to develop and validate an inventory for assessing mood states that can be used with adolescent populations in the context of classroom and athletic environments. There are at least three arguments to suggest that there is a need to develop such a measure.

First, there has been considerable research to examine mood among adolescents. Mood has typically been assessed using the Profile of Mood States (POMS; McNair et al., 1971). The POMS describes six subcomponents of the overall mood construct: Anger, Confusion, Depression, Fatigue, Tension, and Vigour. The factor structure of the POMS and the associated tables of normative values were derived from groups of adult students and psychiatric out-patients. In the test manual, POMS is recommended for use with “subjects aged 18 and older who have had at least some high school education” (McNair et al., 1971, p. 6). No data from individuals under the age of 18 were used in the original validation studies and therefore the degree to which the POMS and its derivatives are suitable for research involving young participants is unknown.

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sport environments (Goss, 1994; Fry et al., 1995), and clinical settings (Walker and Sprague, 1988). Further, Hollander et al. (1995) proposed using the POMS to screen young athletes for mood changes as a pre-cursor for overtraining syndrome (see Budgett, 1990). Given the research interest in mood in adolescents and the extent to which tests of theory rely upon valid measurement, demonstration of the construct validity of mood measures in the population of interest is an imperative.

Second, the original 65-item POMS has been criticised for taking too long to complete (Shacham, 1983; Grove and Prapavessis, 1992; Curren et al., 1995), a criticism which would apply equally to the 72-item bipolar version (Lorr and McNair, 1984). This point is particularly relevant when mood is assessed in an ecologically valid setting, such as before competition or at the start of a lesson, where brevity is paramount. This has contributed to the development of several shortened versions of the POMS (Grove and Prapavessis, 1992; McNair et al., 1992; Shacham, 1983). However, it is important to recognise that completion time is influenced not just by the number of items but also their comprehensibility. Items in the original POMS and its shortened derivatives were generated from the responses of undergraduate students who were all aged 18 years or over. It has also been noted (Grove and Prapavessis, 1992) that some items of the POMS have a distinct North American orientation, such as “bushed” and “blue”, which may require explanation in other cultural contexts.

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confirmatory factor analysis. The traditional method of choice to demonstrate factorial validity has been exploratory factor analysis techniques. Previously, it was suggested that the replication of factors through exploratory analyses among

disparate samples was evidence of factorial validity (Gorsuch, 1983; Kerlinger, 1979). However, contemporary views suggest that a more rigorous procedure to test the generalisability of a measure is to use multisample confirmatory factor analysis (CFA) to test the extent to which data support hypothesised relationships specified in a prior model across a number of different samples (Bentler, 1995; Tabachnick and Fidell, 1996; Thompson and Daniel, 1996). To date, neither the POMS (McNair et al., 1971), nor its shortened versions (Grove and Prapavessis, 1992; McNair et al., 1992; Shacham, 1983) have been scrutinised using either single-sample or multi-sample CFA.

In summary, there is a need for a shortened version of the POMS developed specifically for younger populations and therefore the purpose of the present study was to develop and validate such an inventory.

Research Strategy

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procedures are among the sources of information which contribute to the definition and understanding of the constructs assessed by the self-report measure and therefore act as the basis by which construct validity is judged.

An important research decision in the development of a questionnaire is the number of items included in each factor, particularly when brevity is important. It is suggested that, theoretically, there are an infinite number of items potentially

available for the measurement of any construct (Anastasi and Urbina, 1997).

Consequently, the strategy for identifying a construct typically starts with a relatively large pool of items that is reduced through subsequent analyses. Kline (1993)

cautioned that item reduction might yield a factor containing items with an extremely similar meaning. He suggested that this may lead to two issues regarding the validity of the factor. The first is that the factor might show high validity coefficients, and thereby show evidence of construct validity. Second, the factor might assess a limited dimension of the construct, and therefore the factor should be re-labelled to reflect the dimension of the construct it is assessing. Therefore, in the present study, validation of the inventory was done over a series of different stages with each stage acting as a check on the findings from the previous stage.

As the purpose of the study was to develop a short questionnaire, this raises the question on the optimum number of items needed to assess a construct. Jackson and Marsh (1996) argued that the optimum number of items needed to describe a construct in a short questionnaire is four. Further, Bollen (1989) cautioned against reducing the number of items in a factor to less than three. From a statistical

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above the generally accepted criterion value of 0.70 (Tabachnick and Fidell, 1996). As alpha coefficients are influenced by the number of items in a factor, it is

suggested that each factor of a questionnaire should contain the same number of items to facilitate accurate comparisons of internal consistency. Therefore, the aim of the current research was to produce a version of the POMS with six factors of four items each.

The research process had three stages. First, to establish content validity, the suitability of an initial item pool was assessed by a panel of experts and by school children. Second, to establish factorial validity, the hypothesised factor structure of the item pool was assessed using CFA of the mood responses of school children. A revised model was then tested among samples of school children and young athletes simultaneously using multisample CFA. The third stage, to establish criterion validity, tested the extent to which the subscales of the questionnaire correlated with previously validated measures.

Stage 1: Content Validity

Content validity refers to the extent to which items represent the construct they are purported to measure. A standard approach to establishing content validity is to use experts (e.g., McNair et al., 1971; Martens, et al., 1990) or a representative sample of participants (e.g., Jones et al., 1990) or both (e.g., Widmeyer et al., 1985) to select or confirm items which best describe the construct in question. The

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An initial item pool of 83 mood descriptors was established comprising the 65 adjectives derived from the original POMS plus, where it was suspected that existing items might prove inappropriate for adolescents, 18 additional adjectives taken from a thesaurus. Ten teachers of English at secondary schools identified those adjectives whose meaning they believed “would be understood by the vast majority of children in the 14-16 age group”. Items were eliminated if four or more teachers identified them as inappropriate, resulting in 13 items being discarded.

To maximise comprehensibility among children and retaining the original conceptualisation of the mood construct, a sample of 50 children (age range: 14 - 15 years) rated the extent to which the remaining 70 items described the original factors (Anger, Confusion, Depression, Fatigue, Tension, and Vigour). Participants

identified items that, according to their understanding, were closest in meaning to the six mood factors. The top seven items under each of the six headings were selected for an initial 42-item inventory and were assigned randomly to order. The rationale for using the top seven items was to retain a sufficient number of items to allow further item reduction following CFA among a larger sample. It was also judged, based on the characteristics of the Cronbach alpha estimates described earlier, that each factor should contain the same number of items.

Stage 2.1: Preliminary test of factorial validity

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Anger is typified by feelings which vary in intensity from mild annoyance or aggravation to fury and rage and is associated with arousal of the autonomic nervous system (Spielberger, 1991). Confusion is proposed to be a feeling state characterised by feelings of bewilderment, uncertainty, and is associated with a general failure to control attention and emotions. Depression is associated with a negative self-schema characterised by themes such as hopelessness, personal deficiency, worthlessness, and self-blame (Beck and Clark, 1988). Fatigue is typified by feelings of mental and physical tiredness. Tension is typified by feelings such as nervousness,

apprehension, worry, and anxiety. Vigour is typified by feelings of excitement, alertness, and physical energy.

It was hypothesised that Depression would show moderate positive relationships with Anger, Confusion, Fatigue, and Tension, and a weak inverse relationship with Vigour. Vigour would show a moderate inverse relationship with Fatigue and be unrelated to Anger, Confusion, and Tension. This pattern of

intercorrelations among mood dimensions has been found with both athletes (Grove and Prapavessis, 1992; Terry and Slade, 1995) and students (McNair et al., 1971) when mood is assessed using the “How do you feel right now” response set. A general limitation of mood research in sport is that intercorrelations among POMS dimensions have rarely been reported.

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research strategy should test alternative models of mood, rather than refine the six-factor model. In the present study, alternative models of mood would be explored at stage if the POMS model of mood was rejected.

Method

Participants. Students at a secondary school1 in the suburbs of west London (England) participated in a study to test the factor structure of an initial 42-item and a revised 24-item version of the Profile of Mood States-Adolescents (POMS-A). The school population had a mixed socio-economic and ethnic composition.

The sample comprised 416 children ranging from 14 to 16 years of age (M = 14.8 yr., SD = 1.0 yr.). In 48 cases, participants failed to respond to one or more items and their responses were discarded. Therefore, 368 completed questionnaires (Male = 199, Female = 169) were available for analysis.

Procedure. The inventory was completed by participants in a classroom setting. Participants were asked to rate “How are you feeling right now” in terms of the mood descriptors, e.g., “Worried”, “Unhappy”, etc. Responses were provided on a scale from 0 (“not at all”) to 4 (“extremely”). To ensure consistency during data collection, instructions were read from a prepared script. Further, a culturally-appropriate alternative word list (c.f., Albrecht and Ewing, 1989) was available to participants for reference in case mood descriptors could not be understood although no participants referred to this list.

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Data Analysis.

CFA using EQS V5 (Bentler and Wu, 1995) was used to test the 42-item, six-factor structure of mood. As multivariate non-normality was evidenced (Mardia’s value = 117.50), data were analysed using the Maximum Likelihood (ML) Robust estimation method (see Hoyle, 1995). The model specified that items were related to their hypothesised factor with the variance of the factor fixed to 1. Anger,

Confusion, Depression, Fatigue, and Tension were allowed to inter-correlate. Vigour was allowed to correlate with Depression and Fatigue only, as it was hypothesised that the Vigour-Anger, Vigour-Confusion, and Vigour-Tension relationships would not differ significantly from zero.

According to Hu and Bentler (1995), there is little agreement among

researchers about the best index of the overall fit of a model tested by confirmatory factor analysis. Consequently, it has been suggested that researchers should report a number of different fit indices (Hoyle and Panter, 1995). First, the chi2 : df ratio has been proposed as a superior index to the chi-square because with large samples and complex models there is a tendency for chi-square values to be inflated, causing good fitting models to be rejected erroneously (Byrne, 1989). Although Byrne (1989) suggested that a chi2 : df ratio of < 2 indicates an acceptable fit, researchers have suggested examining fit indices which use the chi-square in conjunction with other fit indices (see Hu and Bentler, 1995 for review).

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regression (Hoyle and Panter, 1995, p. 166). The AGFI is similar to the GFI but takes into account the complexity of the model. Hu and Bentler (1995) contend that the GFI is the most reliable absolute fit index. The criterion value for an acceptable fit is 0.90 for both indices.

Two incremental fit indices were also used to judge factorial validity, the Non-Normed Fit Index (NNFI: Tucker and Lewis, 1973) and the Robust

Comparative Fit Index (RCFI: Bentler, 1990). The NNFI takes into account sample size, hence is suggested to provide a better estimate of the fit of a model than the Normed Fit Index when multivariate normality is violated. The RCFI evaluates the adequacy of the hypothesised model in relation to the worst (independent) model. If the hypothesised model is not a significant improvement on the independent model the fit index will be close to zero (Bentler, 1995). These indices have been found to effectively control for overestimation of chi2, under-estimation of incremental fit indexes, and under-identification of errors when data are not normally distributed (see West, Finch, & Curran, 1995). The criterion value for both incremental fit indices is 0.90 (Bentler, 1995). Finally, the Root Mean Square Error of

Approximation (RMSEA; Steiger, 1990) was used as a measure of the extent to which the model was supported per degree of freedom. Browne and Cudeck (1993) proposed that a value of 0.05 or lower indicates a close fit, and values up to 0.08 represent a reasonable fit.

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produce a more parsimonious version of an existing questionnaire (see Jackson and Marsh, 1996).

Results and Discussion

The purpose of this stage of the research was to test a 42-item and a 24-item six-factor model of mood using confirmatory factor analysis. Results for the 42-item model showed poor fit (GFI = 0.780; AGFI = 0.754; NNFI = 0.823; RCFI = 0.854) except the Satorra-Bentler scaled chi2: df ratio of 1.48. The standardised solution results (see Table 1) offered support for the hypothesised relationships between the majority of the items although several items showed a weak relationship with their hypothesised factor (“Forgetful”, “Lonely”, “Ready to fight”, “Spiteful”; and “Stimulated”).

Following removal of the weakest three items from each scale, CFA results of the revised 24-item POMS-A yielded acceptable fit indices for the NNFI (0.92), RCFI (0.93), and RMSEA (0.06). The GFI (0.89) and AGFI (0.86) were below the .90 criterion value. Cronbach alpha coefficients all exceeded the 0.70 criterion value (see Table 2). Collectively, the results indicated that the 24-item, six-factor model should go forward to the next stage of validation.

Stage 2.2: Test of the generalisability of the factor structure

The establishment of factorial validity involves demonstrating that the

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Method

Participants

Sample 1 comprised 683 young athletes (Age: M = 14.7 yr., SD = 1.8 yr.; Male: n = 301; Female: n = 382). Participants were from the sports of archery, field hockey, judo, netball, soccer, table-tennis, track and field, trampolining, triathlon, and volleyball. Sample 2 comprised 594 school children (M = 14.7 yr., SD = 1.4 yr.; Male = 339, Female = 313).

Procedure

POMS-A was administered 1 hr. Before competition for the athletic sample and at the start or the end of a class lesson for the school children. The remaining procedures used to gather data replicated those used in Stage 2.1 of the research. Results and Discussion

The chi2 : df ratio indicated an acceptable fit of the data to the model in both samples (school children: chi2 : df ratio = 2.06; young athletes: chi2 : df ratio = 2.19). The fit indices provided further support for the fit of the model in both samples with fit indices above the .90 for the GFI (school children = 0.908; young athletes = 0.905), NNFI (school children = 0.919; young athletes = 0.901), RCFI (school children = 0.925; young athletes = 0.912), and RMSEA (school children = 0.052; young athletes = 0.062), although AGFI values were marginally below the .90 criterion value (school children = 0.884; young athletes = 0.881). Alpha coefficients ranged from 0.75 to 0.86 indicating that the factors contain items which are

internally consistent (see Table 2).

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school children sample. Correlation coefficients among mood dimensions were consistent with the hypothesised model (see Table 3). Depression showed moderate to strong positive relationships with Anger, Confusion, Fatigue, and Tension, and a weak inverse relationship with Vigour. Lagrange Multiplier Test results indicated that the fit of the model would not be improved by allowing Vigour to correlate with Anger, Confusion, and Tension.

The strength of the intercorrelations among factors was weaker than those typically reported by researchers using the original POMS (see Grove and

Prapavessis, 1992; McNair et al., 1971, 1992; Terry and Slade, 1995). This suggests that the POMS-A shows greater factorial independence than the original version. Collectively, results strongly supported the factorial validity of the 24-item POMS-A in the two samples independently. The next step in the validation process was to test the hypothesised model using multisample analysis.

Stage 2.3: Multisample Analysis

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In multisample confirmatory factor analysis, it is possible to test a number of different hypotheses regarding the similarity of relationships across samples using the Lagrange Multiplier (LM) Test. In multisample analysis, the LM Test examines the extent to which the fit of the model would be improved if equality constraints were removed. Following recommendations of Bentler (1995), a hierarchical procedure was used to place equality constraints on hypothesised relationships. The first multisample analysis tested the model with no equality in order to get a baseline score on which to compare more restricted models. The second analysis placed equality constraints on factor loadings. The third analysis placed equality constraints on factor loadings and correlation coefficients between factors. It was hypothesised that equality constraints on all relationships would hold between the two groups.

Prior to conducting multisample analyses, differences in the intensity of mood responses between the two samples were examined. Demonstration of differences in the intensity of mood lends support to the notion that the participant groups derive from two different populations. Factor scores were calculated by summing item scores within each factor; multivariate analysis of variance (MANOVA) was then used to assess differences in the intensity of factor scores between the two samples. MANOVA showed significant differences in the intensity of mood responses between school children and young athletes (Wilks’ lambda 6, 1266 = 0.91, p < 0.001, see Table 4). Univariate differences indicated that the group of school children reported significantly higher Confusion, Depression, and Fatigue, but lower Tension and Vigour scores than the young athlete group. Calculation of effect sizes showed that the group differences were relatively small for Confusion,

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Differences between school children and young athletes in the intensity of reported mood were consistent with results from a previous large scale between-group comparison of mood responses (Terry and Lane, in press), which found that adult athletes reported lower Anger, Confusion, Depression, Fatigue, and Tension than adult students but higher Vigour scores. The higher Tension scores reported by the young athletes in the present study can be attributed to the assessment of mood prior to competition for that sample. Terry and Lane (in press) also found that athletes reported higher Tension scores prior to competition than when away from the competition environment. Abele and Brehm (1993) reported that mood changes in competitive sport are typified by a decrease in Tension scores from the beginning to the end of the period of competition. Collectively, the results of the MANOVA confirmed significant variations in reported mood between school children and young athletes, and thus support the notion that the two participant groups

represented disparate populations even though most of the young athletes would also have been school children.

Results and Discussion

Multisample sample CFA results are in Table 5. Results supported the baseline model (GFI = 0.906; CFI = 0.922) and the model which constrained factor loadings to be equal (GFI = 0.901; CFI = 0.918). A test of the extent to which relationships among factors, and relationships between items and factors were equal in the two participants groups showed acceptable fit indices (GFI = 0.900; CFI = 0.916).

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12.207, p < 0.01) and factor loadings for “Annoyed” (chi2 = 18.323, p < 0.01) and “Worn-out” (chi2 = 9.834, p < 0.01) differed significantly. Although these statistics may appear to question the generalisability of the solution, further analysis with the equality constraints removed indicated only a marginally improved fit of the model (see Table 5).

A multisample CFA with constraints imposed on the hypothesised

relationships is proposed to provide a very rigorous test of factorial validity (Bentler, 1995). Results of the present study show that hypothesised relationships remain stable between different participant groups even when the intensity of mood responses differ significantly. Collectively, it is proposed that the POMS-A shows strong evidence of factorial validity for use with school children and young athletes. Stage 3: Test of criterion validity

Criterion validity is defined as the “degree to which scores on a test are related to some recognised standard, or criterion” (Thomas & Nelson, 1990, p. 516). Criterion validity can be demonstrated using either concurrent validity or predictive validity. Concurrent validity is defined as when “a measuring instrument is

correlated with some criterion that is administered at the same time, orconcurrently” (Thomas & Nelson, 1990, p. 515). Concurrent validity is typically examined by correlating scores from two questionnaires that were administered concurrently, with the previously validated questionnaire being the criterion variable (see McNair et al., 1971, 1992). Concurrent validity is inferred from the strength of correlations

between two questionnaires which measure the same construct.

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1988). The criterion measure for the Anger, Confusion, Depression, Fatigue, and Tension scales was the Negative Affect scale from the PANAS. A further criterion measure for Anger was provided by the State-Trait Anger-Expression Inventory (STAXI; Spielberger, 1991).

Participants

A total of 182 participants completed the POMS-A and a second

questionnaire. Ninety-one participants (Age: M = 14.3 yr.; SD = 1.2 yr.) completed the PANAS and the POMS-A. A separate sample of 91 participants (Age: M = 13.3 yr., SD = 0.9 yr.) completed the STAXI and the POMS-A. Participants were school-children at a mixed secondary school in North West London.

Measures of criterion validity

Positive and Negative Affect Schedule.

Watson et al. (1988) developed the PANAS to assess independent markers of positive and negative affect. Items are rated on a 5-point scale anchored by ‘Not at all’ (1) and ‘Extremely’ (5). Examples of Positive Affect items include “Excited”, “Enthusiastic”, and “Determined”. Examples of negative mood items include “Distressed”, “Guilty”, and “Scared”.

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children. Consequently the PANAS is an excellent research tool to test the criterion validity of the POMS-A.

State-Trait Anger-Expression Inventory (STAXI).

The factor structure of the 10-item State-Anger scale was validated by Spielberger (1991) using a sample of 550 individuals. Factor analysis yielded a single factor with a Cronbach alpha coefficient of 0.93. Items are rated on a 4-point scale anchored by “Almost never” (1) and “Very Often” (4).

Procedure

Participants completed the questionnaires in accordance with the procedure used in Stages 2.1 and 2.2. Pearson product moment correlation was used to assess the relationship between POMS-A scores and the criterion measure.

Results and Discussion

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General Discussion

The purpose of the study was to develop and validate a shortened version of the POMS suitable for use with children in the context of classroom and athletic environments. Theory testing and construct measurement are inextricably linked. It is suggested that the development of a valid measure of the theoretical construct to be examined should be the first step in the research process (Hendrick & Hendrick, 1986). If the construct validity of the instrument is questionable, then it is not possible to accurately test the theory under investigation. Watson et al. (1988) argued that poorly developed and validated mood inventories contributed to the ambiguity of research findings surrounding the nature of mood in the psychology literature. The three stage validation process used in the present paper tested content validity, factorial validity, and criterion validity and has provided strong support for the 24-item version of the POMS. Collectively, it is proposed that the POMS-A shows clear evidence of construct validity. However, as validation is an ongoing process (Anastasi & Urbina, 1997), it is suggested that future research should continue to investigate the validity of the POMS-A. A limitation of the present concurrent validity process was that the STAXI criterion measures were validated on samples from student rather than athletic populations. Therefore, it is suggested that researchers investigate the validity of measures such as the STAXI (Spielberger, 1991) in sport. Indeed, there is a need for more thorough validation in the sport environment of many of the inventories used in sport psychology research which have been validated on other populations (Schutz, 1994).

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Out of the 24-items on the POMS-A, 17-items are also on the original POMS (“active”, “alert”, “angry”, “annoyed”, “anxious”, “bad-tempered”, “bitter”,

“confused”, “energetic”, “exhausted”, “lively”, “miserable”, “muddled”, “nervous”, “panicky”, “uncertain”, “unhappy”, and “worn-out”). There were 14-items discarded from the POMS-A which are on the original POMS (“bushed”, “cheerful”,

“forgetful”, “furious”, “gloomy”, “lonely”, “on-edge”, “ready to fight”, “sad”, “spiteful”, “tense”, “unable to concentrate”, “uneasy”, and “weary”). Previous research has questioned whether athletes interpret items such as “ready to fight” literally (Terry and Slade, 1995). Additionally, “cheerful” appears to be part of a construct labelled happiness, rather than vigour. In the present study, vigour comprises items associated with positive feelings and high arousal (active, alert, energetic, and lively), which appear to be more appropriate descriptors of the vigour construct.

Uses of the POMS-A

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The POMS-A may provide a useful tool for applied sport psychology research as it is short, easy to complete, and suitable for use with adolescents. Consequently, the POMS-A can be used to assess mood shortly prior to competition without excessively disturbing athlete’s normal pre-competition routines. The brevity of the questionnaire also means that it provides an instrument for assessing mood changes in education settings and for screening young athletes for mood disturbance as a pre-cursor to overtraining syndrome. The POMS-A is contained in the Appendix and researchers are invited to use the scale without written permission from the authors.

Conclusions

In conclusion, the purpose of the study was to develop and validate a measure of mood for adolescents. CFA supported the factorial validity of a 24-item six-factor model using both independent and multisample analyses. Criterion validity was demonstrated through correlating POMS-A scores with previously validated

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Author Note

Peter C. Terry, Andrew M. Lane, Helen, J. Lane, and Lee Keohane, Department of Sport Sciences, Brunel University.

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[image:33.595.110.542.143.750.2]

Table 1 Standardised solution for factor loadings of the 42-item version of the POMS-A

Item Factor loading Error variance

Worried .738 .675

Nervous .716 .698

Anxious .655 .755

Panicky .572 .820

On edge .558 .830

Tense .528 .849

Stressed .524 .852

Unhappy .831 .556

Miserable .807 .590

Depressed .763 .646

Downhearted .684 .730

Sad .648 .762

Gloomy .624 .781

Lonely .398 .918

Energetic .872 .489

Active .868 .496

Lively .725 .689

Alert .607 .795

Cheerful .468 .884

Brisk .417 .909

Stimulated 370 .929

Mixed-up .847 .532

Confused .708 .707

Uncertain 707 .707

Muddled .677 .736

Uneasy .513 .858

Unable to concentrate .372 .928

Forgetful .250 .968

Angry .805 .593

Bitter .756 .654

Annoyed .755 .656

Bad-tempered .692 .722

Furious .651 .759

Spiteful .432 .902

Ready to fight .415 .910

Tired .887 .461

Sleepy .848 .529

Exhausted .786 .618

Worn-out .782 .624

Ready for bed .710 .704

Bushed .694 .720

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[image:34.595.113.545.119.313.2]

Table 2 Internal consistency estimates of mood dimensions among three samples

Sample A: School children (N = 369)

42-items 24-items

Young athletes (N = 683)

School children (N = 594)

Anger .80 .82 .80 .80

Confusion .76 .83 .86 .81

Depression .86 .85 .85 .85

Fatigue .90 .90 .82 .85

Tension .82 .74 .75 .82

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[image:35.595.108.539.132.474.2]

Table 3 Inter-correlations of POMS-A subscales among school children and young athletes

Anger Confusion Depression Fatigue

Confusion Young athletes .588

School children .613

Depression Young athletes .876 .700

School children .802 .731 Fatigue

Young athletes .363 .388 .285

School children .275 .309 .382

Tension

Young athletes .186 .492 .292 .174

School children .316 .511 .443 .250

Vigour

Young athletes -.079 -.288

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[image:36.595.109.549.118.355.2]

Table 4 POMS-A scores among school children and young athletes School children

(N = 594) M SD

Young athletes (N = 683)

M SD F 1271

Effect Size

Anger 1.52 2.53 1.26 2.30 3.47 .11

Confusion 2.12 2.85 1.66 2.54 9.68** .17

Depression 1.90 2.98 1.36 2.58 8.21* .20

Fatigue 5.07 3.77 3.23 3.19 41.64* .53

Tension 2.85 3.46 3.63 3.17 17.56* .24

Vigour 6.90 4.05 8.76 4.14 65.35* .45

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[image:37.595.110.546.146.427.2]

Table 5 Multisample confirmatory factor analysis of the POMS-A among school children and young athletes

Fit Statistics Unconstrained model (df = 480)

Factor loadings (df = 540)

Factor loadings and correlations (df = 516)

Chi2 : df ratio 3.275 3.055 3.254

Goodness of Fit Index .906 .901 .900

Adjusted Goodness of

Fit Index .941 .941 .942

Nonnormed Fit Index .910 .910 .911

Comparative Fit Index .922 .918 .916

Root Mean Square

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[image:38.595.115.560.121.289.2]

Table 6 Correlations between POMS-A scores and criterion measures

Criterion Measure Anger Confusion Depression Fatigue Tension Vigou r State Anger

Expression Inventory .82* .23 35* .36* 29* -.03

Positive Affect .16 .13 .10 .01 .21 .62*

Negative Affect .78* .80* .75* .52* .72* -.01

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Appendix

The Profile of Mood States-C

Below is a list of words that describe feelings people have. Please read each one carefully. Then circle the answer which best describes HOW YOU FEEL RIGHT NOW. Make sure you answer every question.

Not at all A little Moderately Quite a bit Extremely

1. Panicky 0 1 2 3 4

2. Lively 0 1 2 3 4

3. Confused 0 1 2 3 4

4. Worn out 0 1 2 3 4

5. Depressed 0 1 2 3 4

6. Downhearted 0 1 2 3 4

7. Annoyed 0 1 2 3 4

8. Exhausted 0 1 2 3 4

9. Mixed- up 0 1 2 3 4

10.Sleepy 0 1 2 3 4

11.Bitter 0 1 2 3 4

12.Unhappy 0 1 2 3 4

13.Anxious 0 1 2 3 4

14.Worried 0 1 2 3 4

15.Energetic 0 1 2 3 4

16.Miserable 0 1 2 3 4

17.Muddled 0 1 2 3 4

18.Nervous 0 1 2 3 4

19.Angry 0 1 2 3 4

20.Active 0 1 2 3 4

21.Tired 0 1 2 3 4

22.Bad tempered 0 1 2 3 4

23.Alert 0 1 2 3 4

Figure

Table 1  Standardised solution for factor loadings of the 42-item version of the
Table 2  Internal consistency estimates of mood dimensions among three samples
Table 3  Inter-correlations of POMS-A subscales among school children and young
Table 4  POMS-A scores among school children and young athletes
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